CN110045734A - Method, apparatus and computer equipment are determined based on the parameters weighting of path planning - Google Patents
Method, apparatus and computer equipment are determined based on the parameters weighting of path planning Download PDFInfo
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Abstract
The present invention proposes that a kind of parameters weighting based on path planning determines method, device and computer equipment, wherein, method includes: a variety of candidate weight combinations according to vehicle driving parameter, generate multiple input information, multiple input information are inputted into path planning model respectively, obtain corresponding a plurality of path candidate, determine the similarity between a plurality of path candidate and practical driving path, according to similarity, from a variety of candidate weight combinations, determine that target weight combines, it realizes and is combined according to a variety of candidate weights, based on the similarity between the obtained path of planning and practical driving path, the weight of the determination vehicle driving parameter of automation, the accuracy that the weight of vehicle driving parameter determines is improved simultaneously, robustness is also preferable, solve the parameters weighting that can not automatically determine vehicle driving in the prior art, vehicle driving ginseng simultaneously Several accuracys and robustness is poor, so that path planning accuracy is lower, is applicable in the limited technical problem of scene.
Description
Technical field
The present invention relates to automatic Pilot technical field more particularly to a kind of parameters weighting determination sides based on path planning
Method, device and computer equipment.
Background technique
In unmanned technology, path planning is the key link in unmanned technology, and the parameter of path planning
Weight is undoubtedly the most important thing of path planning link.
In the prior art, vehicle is manually arranged one is artificial experience is based in the determination method of path planning parameters weighting
The weight of driving parameters, the method efficiency that this parameters weighting determines is lower, and another kind is to carry out vehicle by neural network
The weights of driving parameters determines, this mode needs the labeled data of magnanimity, the inadequate robustness of algorithm, and vehicle driving parameter is true
Fixed accuracy is lower, while poor robustness, so that application scenarios are limited.
Summary of the invention
The present invention is directed to solve at least some of the technical problems in related technologies.
For this purpose, the first purpose of this invention is to propose that a kind of parameters weighting based on path planning determines method, lead to
Cross the similarity determined between path planning model obtained a plurality of path candidate and practical driving path, according to similarity from
The target weight combination that vehicle driving parameter is determined in a variety of candidate's weight combinations, realizes based on path planning, automation
Determine the weight of vehicle driving parameter, at the same improve vehicle driving parameter weight determine accuracy, robustness also compared with
It is good.
Second object of the present invention is to propose a kind of parameters weighting determining device based on path planning.
Third object of the present invention is to propose a kind of computer equipment.
Fourth object of the present invention is to propose a kind of non-transitorycomputer readable storage medium.
In order to achieve the above object, first aspect present invention embodiment proposes a kind of parameters weighting determination based on path planning
Method, comprising:
According to a variety of candidate weight combinations of vehicle driving parameter, multiple input information are generated；
The multiple input information is inputted into path planning model respectively, obtains corresponding a plurality of path candidate；
Determine the similarity between a plurality of path candidate and practical driving path；
Determine that target weight combines from a variety of candidate weight combinations according to the similarity.
In order to achieve the above object, second aspect of the present invention embodiment proposes a kind of parameters weighting determination based on path planning
Device, comprising:
Generation module generates multiple input information for a variety of candidate weight combinations according to vehicle driving parameter；
Input module obtains corresponding a plurality of time for the multiple input information to be inputted path planning model respectively
Routing diameter；
First determining module, for determining the similarity between a plurality of path candidate and practical driving path；
Second determining module, for determining target weight from a variety of candidate weight combinations according to the similarity
Combination.
In order to achieve the above object, third aspect present invention embodiment proposes a kind of computer equipment, including memory, processing
Device and storage on a memory and the computer program that can run on a processor, when the processor executes described program, reality
Now the parameters weighting based on path planning as described in aforementioned aspects embodiment determines method.
To achieve the goals above, fourth aspect present invention embodiment proposes a kind of computer-readable storage of non-transitory
Medium is stored thereon with computer program, when which is executed by processor, realizes the base as described in aforementioned aspects embodiment
Method is determined in the parameters weighting of path planning.
Technical solution provided by the embodiment of the present invention may include it is following the utility model has the advantages that
According to a variety of candidate weight combinations of vehicle driving parameter, multiple input information are generated, by multiple input information point
Not Shu Ru path planning model, obtain corresponding a plurality of path candidate, determine between a plurality of path candidate and practical driving path
Similarity from a variety of candidate weights combinations, determine that target weight combines, realize according to a variety of candidates according to similarity
Weight combination, based on the similarity between the obtained path of planning and practical driving path, the determination vehicle driving ginseng of automation
Several weights, while the accuracy that the weight for improving vehicle driving parameter determines, robustness are also preferable.
The additional aspect of the present invention and advantage will be set forth in part in the description, and will partially become from the following description
Obviously, or practice through the invention is recognized.
Detailed description of the invention
Above-mentioned and/or additional aspect and advantage of the invention will become from the following description of the accompanying drawings of embodiments
Obviously and it is readily appreciated that, in which:
Fig. 1 determines the process signal of method for a kind of parameters weighting based on path planning provided by the embodiment of the present invention
Figure；
Fig. 2 is the schematic illustration that similarity of paths is determined provided by the embodiment of the present invention；
Fig. 3 is that another based on the parameters weighting of path planning determines that the process of method is shown provided by the embodiment of the present invention
It is intended to；
Fig. 4 is a kind of structural representation of the parameters weighting determining device based on path planning provided in an embodiment of the present invention
Figure；And
Fig. 5 shows the block diagram for being suitable for the exemplary computer device for being used to realize the application embodiment.
Specific embodiment
The embodiment of the present invention is described below in detail, examples of the embodiments are shown in the accompanying drawings, wherein from beginning to end
Same or similar label indicates same or similar element or element with the same or similar functions.Below with reference to
The embodiment of attached drawing description is exemplary, it is intended to is used to explain the present invention, and is not considered as limiting the invention.
Below with reference to the accompanying drawings the parameters weighting based on path planning for describing the embodiment of the present invention determines method, apparatus and meter
Calculate machine equipment.
Fig. 1 determines the process signal of method for a kind of parameters weighting based on path planning provided by the embodiment of the present invention
Figure.
As shown in Figure 1, method includes the following steps:
Step 101, according to a variety of candidate weight combinations of vehicle driving parameter, multiple input information are generated.
Wherein, vehicle driving parameter includes: one in traveling-position, car speed, vehicle acceleration and vehicle yaw angle
A or multiple combinations.Wherein, the traveling-position of vehicle, for example, being obtained by the image analysis of the vehicle of camera shooting
Traveling-position is acquired by the traveling-position of vehicle mounted guidance model and the collected vehicle of global positioning system, or by radar
The traveling-position etc. of the vehicle arrived.Car speed and acceleration, for example, it may be being detected by velocity and acceleration sensor
It obtains.The yaw angle of vehicle, such as can be determining by the detection of gyroscope.Vehicle driving parameter can also include
By the wheel speed information for the vehicle that the wheel speed meter of vehicle provides, the real-time displacement for the vehicle that inertial sensor IMU is provided and rotation
Information etc., vehicle driving parameter are not limited in above-mentioned parameter, no longer enumerate one by one in the present embodiment.
In the embodiment of the present invention, path planning is carried out according to the corresponding detected value of the driving parameters of vehicle, is planned
The weight in different path and vehicle driving parameter has a substantial connection, and each vehicle driving parameter can correspond to it is multiple
Candidate weight, determining vehicle driving parameters weighting is different, carries out the planning road that path planning obtains according to path planning model
Diameter is then different, therefore, it is necessary to determine a variety of candidate weight combinations according to the weight of vehicle driving parameter, generates corresponding more
A input information.Specifically, according under corresponding Driving Scene, the corresponding weight of vehicle driving parameter generates a variety of candidate weights
Combination can be sampled by the way of stochastical sampling as a kind of possible implementation and determine a variety of candidate weight combinations,
, can be to the corresponding weight of current vehicle driving parameters as alternatively possible implementation, traversal obtains all possible
Candidate weight combination generates corresponding multiple input information according to a variety of candidate weight combinations of vehicle driving parameter.
Citing, to put it more simply, being illustrated for comprising 2 vehicle driving parameters, vehicle under overpass Driving Scene
The corresponding candidate weight of driving parameters A is (w1, w2, w3, w4), the corresponding candidate weight of vehicle driving parameter B be (n1,
N2, n3), thus, the candidate weight combination obtained by way of traversal is 6 kinds total, such as (w1, n1), (w1, n2) etc.,
It is not listed one by one herein.
It is to be appreciated that the number of the corresponding each candidate weight of each vehicle driving parameter may be the same or different,
It is not construed as limiting in the present embodiment.
Step 102, multiple input information are inputted into path planning model respectively, obtain corresponding a plurality of path candidate.
In the embodiment of the present invention, path planning model can be for example the greatest hope in Apollo frame
(Expectation Maximization Algorithm, EM) algorithm or LatticePlanner planning algorithm.
Specifically, according to corresponding Driving Scene, the multiple input information that will be generated under corresponding scene input path planning mould
In type, the corresponding a plurality of path candidate of path planning model output is obtained.
Step 103, the similarity between a plurality of path candidate and practical driving path is determined.
In the embodiment of the present invention, it is thus necessary to determine that the similar journey of obtained a plurality of path candidate and artificial practical driving path
Degree, Fig. 2 is the schematic illustration that similarity of paths is determined provided by the embodiment of the present invention, as shown in Fig. 2, in figure schematically
The path A and path B shown is two determining path candidates, and the virtual image in figure is artificial practical driving path, when
Obtained a plurality of path candidate and artificial practical driving path is closer, that is to say, that path candidate and artificial reality drive road
The coincidence degree of diameter is higher, then illustrates that path candidate is more similar to artificial practical driving path, the path candidate obtained from
Corresponding candidate's weight combination then can accurately more cook up most reasonable path.
Specifically, it is determined that the similarity under a plurality of path candidate and the Driving Scene between artificial practical driving path, makees
For a kind of possible implementation, can be determined using dynamic time consolidation algorithm ((Dynamic Time Warping))
The similarity degree of path candidate and artificial practical driving path.
Step 104, determine that target weight combines from a variety of candidate weight combinations according to similarity.
It will according to the similarity between a plurality of path candidate and practical driving path as a kind of possible implementation
It is higher than the path candidate of preset threshold with artificial practical driving path similarity, is determined as waiting target for target candidate path
The corresponding candidate weight combination of routing diameter is determined as target weight combination.
It should be noted that different Driving Scenes all have corresponding target weight combination, that is to say, that different driving
The corresponding target weight combination of scene is different.
The parameters weighting based on path planning of the embodiment of the present invention determines in method, according to a variety of of vehicle driving parameter
Candidate weight combination, generates multiple input information, multiple input information is inputted path planning model respectively, are obtained corresponding
A plurality of path candidate determines the similarity between a plurality of path candidate and practical driving path, according to similarity, from a variety of times
It selects in weight combination, determines that target weight combines, realize the parameters weighting for automatically determining vehicle driving based on path planning,
The accuracy that the parameters weighting of vehicle driving determines is improved simultaneously, and robustness is also preferable, and solution in the prior art can not be automatic
The parameters weighting of vehicle driving is determined, while the accuracy of vehicle driving parameter and robustness are poor, so that path planning is accurate
It spends lower, is applicable in the limited technical problem of scene.
For an embodiment in clear explanation, present embodiments provides another parameters weighting based on path planning and determine
The possible implementation of method, Fig. 3 are that parameters weighting of the another kind based on path planning provided by the embodiment of the present invention is true
Determine the flow diagram of method.
As shown in figure 3, this method may comprise steps of:
Step 301, it determines the Driving Scene of vehicle, and inquires and obtain the corresponding power of each vehicle driving parameter under Driving Scene
Weight.
Wherein, the Driving Scene of vehicle, for example, Driving Scene under overpass, the Driving Scene on expressway are either
The Driving Scene etc. of fork in the road, Driving Scene more implement in be not listed one by one.
Specifically, according to the Driving Scene of vehicle, inquiry obtains corresponding under the Driving Scene, and each vehicle driving parameter is corresponding
Weight, that is to say, that under different Driving Scenes, the corresponding weight of each vehicle driving parameter is then different, thus corresponding mesh
It is also different to mark weight combination.
Step 302, the corresponding multiple groups detected value of vehicle driving parameters under the Driving Scene is determined.
Wherein, every group of detected value includes the value of each moment vehicle driving parameter, for example, initial time t0, the position of vehicle
It is set to p1, the speed of vehicle is s1, the acceleration of vehicle is a1, the yaw angle of vehicle is w1Deng.
In the embodiment of the present invention, in order to improve the accuracy that vehicle driving parameters weight determines under corresponding Driving Scene, obtain
The multiple groups detected value for taking vehicle driving parameters under corresponding Driving Scene can be as a kind of possible implementation from first
In the database that vehicle traveling information under the Driving Scene of acquisition generates, the multiple groups detected value of vehicle driving parameter is obtained；
As alternatively possible implementation, can be according to the generation model in deep learning, such as automatic variation encoding model
(Variational Autoencoders), the corresponding detected value input of vehicle driving parameter under the scene that will acquire generate
In model, the corresponding multiple groups detected value of vehicle driving parameter under Driving Scene similar with the Driving Scene is exported, will be generated
The multiple groups approx imately-detecting value and vehicle detection that model generates obtain detected value, corresponding as vehicle driving parameters under the scene
Multiple groups detected value the determining Driving Scene can be improved by increasing the quantity of the corresponding detected value of vehicle driving parameter
Target weight accuracy.
Step 303, according to a variety of candidate weight combinations of vehicle driving parameter, multiple input information are generated.
Specifically, under corresponding Driving Scene, the corresponding a variety of candidate weight combinations of vehicle driving parameter are determined respectively,
Wherein, the determination method of candidate weight combination is referred to the step 101 in an embodiment, and principle is identical, no longer superfluous herein
It states.In turn, according to a variety of candidate weight combinations of the vehicle driving parameter determined under corresponding Driving Scene, join in vehicle driving
When number takes each group of detected value, the corresponding multiple input information of each group of detected value are generated.
Step 304, multiple input information are inputted into path planning model respectively, obtain corresponding a plurality of path candidate.
Wherein, each input information corresponds to a kind of weight combination.
In the embodiment of the present invention, the multiple input information generated when vehicle driving parameter is taken each group of detected value, respectively
Input path planning model, when determining that vehicle driving parameter takes each group of detected value, corresponding a plurality of path candidate.
As a kind of possible implementation, for one group of detected value, path planning model determines the initial time of vehicle
State, such as initial time state is denoted as S0, original state S0Driving parameters comprising the corresponding vehicle of the state, ascend the throne
It sets, speed, acceleration and yaw angle etc., in turn, path planning model calculates a variety of candidate weights according to path planning algorithm
Corresponding a plurality of path candidate under combination, the equally driving parameters of the vehicle comprising per moment, i.e. position, speed in path candidate
Degree, acceleration and yaw angle etc..For example, as indicated with 2, a plurality of path candidate under the available scene.It in turn, can be true
When making that vehicle driving parameters take each group of detected value under the Driving Scene, corresponding a plurality of path candidate.
Step 305, the similarity between a plurality of path candidate and practical driving path is determined.
Specifically, when taking each group of detected value for vehicle driving parameter, corresponding a plurality of path candidate, calculate separately with
Similarity between practical driving path.
Wherein, about the specific determining method of similarity, the step 103 being referred in an embodiment, principle is identical,
Details are not described herein again.
Step 306, determine that target weight combines from a variety of candidate weight combinations according to similarity.
Specifically, it is determined that similarity meets the candidate weight of setting condition when vehicle driving parameter takes each group of detected value
Combination, for example, selection similarity value maximum first 3 candidate weight combinations, obtain the corresponding all symbols of vehicle driving parameter
The candidate weight combination to impose a condition is closed, i.e., corresponding multiple candidate weight groups when vehicle driving parameter takes all groups of detected values
It closes, in turn, meets similarity the candidate weight combination statistics frequency of occurrences of setting condition, according to the frequency of occurrences, from similarity
Meet in the candidate weight combination of setting condition, determine that target weight combines, as a kind of possible implementation, according to out
The highest candidate weight combination of the frequency of occurrences is determined as target weight combination by existing frequency；As alternatively possible realization side
Formula determines that the frequency of occurrences meets multiple candidate weights combinations of preset condition according to the frequency of occurrences, and according to it is preset go out
The weight of existing frequency, is weighted, and corresponding candidate weight combination is calculated, regard candidate's weight combination as mesh
Weight combination is marked, in the candidate weight combination by meeting preset condition from similarity, the further screening frequency of occurrences meets
The candidate weight combination of preset condition is combined as target weight, improves the determining accuracy of target weight combination.
Step 307, it is combined according to target weight, adjusts the weight of vehicle driving parameter, according to the vehicle row of adjustment weight
It sails parameter and carries out path planning.
Specifically, it is combined, is adjusted under corresponding Driving Scene, vehicle according to the determining corresponding current weight of each Driving Scene
The weight of driving parameters realizes the weight for automatically determining vehicle driving parameter, and the weight of the vehicle driving parameter determined is quasi-
Exactness is higher.In turn, the path planning that automatic Pilot is carried out according to the vehicle driving parameter of adjustment weight improves vehicle certainly
The accuracy of dynamic driving path planning, improves traffic safety, user satisfaction is also higher.
It is to be appreciated that different Driving Scenes, the corresponding target weight combination of vehicle driving parameter is different, for example,
More fork on the road is often arranged in scene under overpass, and the sight of driver will receive certain influence, therefore high
The weight of lower car speed of building bridge then should not be very high, that is to say, that the speed of vehicle can not ether it is high, compare, at a high speed
The scene on road, the corresponding weight of the driving parameters of car speed then can be higher, and vehicle is run at high speed.
The parameters weighting based on path planning of the embodiment of the present invention determines in method, according to corresponding Driving Scene, really
The detected value of vehicle driving parameters, is generated under similar Driving Scene using automatic variation encoding model under the fixed Driving Scene
Multiple groups approx imately-detecting value and vehicle detection that model generates are obtained detected value, as under the scene by multiple groups approx imately-detecting value
The corresponding multiple groups detected value of vehicle driving parameter obtains a plurality of candidate road using path planning model using the multiple groups detected value
Diameter meets multiple candidate weight groups of preset condition from similarity between driving path determining and practical in a plurality of path candidate
It closes, and meets similarity the candidate weight combination statistics frequency of occurrences of setting condition, according to the high candidate weight of the frequency of occurrences
The target weight combination determined under the scene is combined, improving the determining accuracy of target weight combination can similarly determine
Target weight combination under each Driving Scene carries out vehicle driving parameters under corresponding Driving Scene according to target weight combination
Adjustment, realize and automatically determine vehicle driving parameter, and determine vehicle driving parameter accuracy it is higher.
In order to realize above-described embodiment, the present invention also proposes a kind of parameters weighting determining device based on path planning.
Fig. 4 is a kind of structural representation of the parameters weighting determining device based on path planning provided in an embodiment of the present invention
Figure.
As shown in figure 4, the device includes: generation module 41, input module 42, the first determining module 43 and the second determination
Module 44.
Generation module 41 generates multiple input information for a variety of candidate weight combinations according to vehicle driving parameter.
Input module 42 obtains corresponding a plurality of candidate for multiple input information to be inputted path planning model respectively
Path.
First determining module 43, for determining the similarity between a plurality of path candidate and practical driving path.
Second determining module 44, for determining that target weight combines from a variety of candidate weight combinations according to similarity.
As a kind of possible implementation of the embodiment of the present invention, described device further include:
Module is adjusted, for combining according to the target weight, adjusts the weight of the vehicle driving parameter；
Planning module, for carrying out path planning according to the vehicle driving parameter of adjustment weight.
As a kind of possible implementation, each Driving Scene is combined with corresponding target weight, above-mentioned adjustment module,
For combining according to the corresponding target weight of each Driving Scene, the power of the vehicle driving parameter under corresponding Driving Scene is adjusted
Weight.
As a kind of possible implementation, vehicle driving parameter has the multiple groups detected value of corresponding similar Driving Scene；
Above-mentioned second determining module 44, comprising:
First determination unit, for determine the vehicle driving parameter take each group described in detected value when, the similarity
Meet the candidate weight combination of setting condition.
Statistic unit, the candidate weight for meeting the similarity setting condition combine the statistics frequency of occurrences.
Second determination unit, for meeting the candidate weight of setting condition from the similarity according to the frequency of occurrences
In combination, the target weight combination is determined.
As a kind of possible implementation, above-mentioned first determining module 43, for using dynamic time warping algorithm, really
Similarity between the fixed a plurality of path candidate and practical driving path.
As a kind of possible implementation, the vehicle driving parameter includes: that traveling-position, car speed, vehicle add
One or more combinations in speed and vehicle yaw angle.
It should be noted that the aforementioned parameters weighting to based on path planning determines that the explanation of embodiment of the method is also fitted
For the parameters weighting determining device of the embodiment, principle is identical, and details are not described herein again.
In the parameters weighting determining device based on path planning of the embodiment of the present invention, according to a variety of of vehicle driving parameter
Candidate weight combination, generates multiple input information, multiple input information is inputted path planning model respectively, are obtained corresponding
A plurality of path candidate determines the similarity between a plurality of path candidate and practical driving path, according to similarity, from a variety of times
It selects in weight combination, determines that target weight combines, realize the parameters weighting for automatically determining vehicle driving based on path planning,
The accuracy that the parameters weighting of vehicle driving determines is improved simultaneously, and robustness is also preferable.
In order to realize above-described embodiment, the embodiment of the present invention also proposed a kind of computer equipment, including memory, processing
Device and storage on a memory and the computer program that can run on a processor, when the processor executes described program, reality
Now the parameters weighting based on path planning as described in preceding method embodiment determines method.
Fig. 5 shows the block diagram for being suitable for the exemplary computer device for being used to realize the application embodiment.What Fig. 5 was shown
Computer equipment 12 is only an example, should not function to the embodiment of the present application and use scope bring any restrictions.
As shown in figure 5, computer equipment 12 is showed in the form of universal computing device.The component of computer equipment 12 can be with
Including but not limited to: one or more processor or processing unit 16, system storage 28 connect different system components
The bus 18 of (including system storage 28 and processing unit 16).
Bus 18 indicates one of a few class bus structures or a variety of, including memory bus or Memory Controller,
Peripheral bus, graphics acceleration port, processor or the local bus using any bus structures in a variety of bus structures.
For example, these architectures include but is not limited to industry standard architecture (Industry Standard
Architecture；Hereinafter referred to as: ISA) bus, microchannel architecture (Micro Channel Architecture；With
Lower abbreviation: MAC) bus, enhanced isa bus, Video Electronics Standards Association (Video Electronics Standards
Association；Hereinafter referred to as: VESA) local bus and peripheral component interconnection (Peripheral Component
Interconnection；Hereinafter referred to as: PCI) bus.
Computer equipment 12 typically comprises a variety of computer system readable media.These media can be it is any can be by
The usable medium that computer equipment 12 accesses, including volatile and non-volatile media, moveable and immovable Jie
Matter.
Memory 28 may include the computer system readable media of form of volatile memory, such as random access memory
Device (Random Access Memory；Hereinafter referred to as: RAM) 30 and/or cache memory 32.Computer equipment 12 can
To further comprise other removable/nonremovable, volatile/non-volatile computer system storage mediums.Only as act
Example, storage system 34 can be used for reading and writing immovable, non-volatile magnetic media, and (Fig. 5 does not show that commonly referred to as " hard disk drives
Dynamic device ").Although being not shown in Fig. 5, the magnetic for reading and writing to removable non-volatile magnetic disk (such as " floppy disk ") can be provided
Disk drive, and to removable anonvolatile optical disk (such as: compact disc read-only memory (Compact Disc Read Only
Memory；Hereinafter referred to as: CD-ROM), digital multi CD-ROM (Digital Video Disc Read Only
Memory；Hereinafter referred to as: DVD-ROM) or other optical mediums) read-write CD drive.In these cases, each drive
Dynamic device can be connected by one or more data media interfaces with bus 18.Memory 28 may include at least one journey
Sequence product, the program product have one group of (for example, at least one) program module, these program modules are configured to perform this Shen
Please each embodiment function.
Program/utility 40 with one group of (at least one) program module 42 can store in such as memory 28
In, such program module 42 include but is not limited to operating system, one or more application program, other program modules with
And program data, it may include the realization of network environment in each of these examples or certain combination.Program module 42 is logical
Often execute the function and/or method in embodiments described herein.
Computer equipment 12 can also be with one or more external equipments 14 (such as keyboard, sensing equipment, display 24
Deng) communication, can also be enabled a user to one or more equipment interact with the computer equipment 12 communicate, and/or with make
The computer equipment 12 any equipment (such as network interface card, the modulatedemodulate that can be communicated with one or more of the other calculating equipment
Adjust device etc.) communication.This communication can be carried out by input/output (I/O) interface 22.Also, computer equipment 12 is also
Network adapter 20 and one or more network (such as local area network (Local Area Network can be passed through；Following letter
Claim: LAN), wide area network (Wide Area Network；Hereinafter referred to as: WAN) and/or public network, for example, internet) communication.
As shown, network adapter 20 is communicated by bus 18 with other modules of computer equipment 12.Although should be understood that figure
In be not shown, can in conjunction with computer equipment 12 use other hardware and/or software module, including but not limited to: microcode,
Device driver, redundant processing unit, external disk drive array, RAID system, tape drive and data backup storage
System etc..
Processing unit 16 by the program that is stored in system storage 28 of operation, thereby executing various function application and
Data processing, such as realize the paths planning method referred in previous embodiment.
In order to realize above-described embodiment, the embodiment of the present invention also proposed a kind of non-transitory computer-readable storage medium
Matter is stored thereon with computer program, when which is executed by processor realize as described in preceding method embodiment based on road
The parameters weighting of diameter planning determines method.
In the description of this specification, reference term " one embodiment ", " some embodiments ", " example ", " specifically show
The description of example " or " some examples " etc. means specific features, structure, material or spy described in conjunction with this embodiment or example
Point is included at least one embodiment or example of the invention.In the present specification, schematic expression of the above terms are not
It must be directed to identical embodiment or example.Moreover, particular features, structures, materials, or characteristics described can be in office
It can be combined in any suitable manner in one or more embodiment or examples.In addition, without conflicting with each other, this field
Technical staff can carry out the feature of different embodiments or examples described in this specification and different embodiments or examples
Combination and combination.
In addition, term " first ", " second " are used for descriptive purposes only and cannot be understood as indicating or suggesting relative importance
Or implicitly indicate the quantity of indicated technical characteristic.Define " first " as a result, the feature of " second " can be expressed or
Person implicitly includes at least one this feature.In the description of the present invention, the meaning of " plurality " is at least two, such as two,
Three etc., unless otherwise specifically defined.
Any process described otherwise above or method description are construed as in flow chart or herein, and expression includes
It is one or more for realizing custom logic function or process the step of the module of code of executable instruction, segment or
Part, and the range of the preferred embodiment of the present invention includes other realization, wherein can not press shown or discussed
Sequentially, including according to related function by it is basic simultaneously in the way of or in the opposite order, Lai Zhihang function, this should be by this
The embodiment person of ordinary skill in the field of invention is understood.
Expression or logic and/or step described otherwise above herein in flow charts, for example, being considered use
In the order list for the executable instruction for realizing logic function, may be embodied in any computer-readable medium, for
Instruction execution system, device or equipment (such as computer based system, including the system of processor or other can be from instruction
Execute system, device or equipment instruction fetch and the system that executes instruction) use, or combine these instruction execution systems, device or
Equipment and use.For the purpose of this specification, " computer-readable medium ", which can be, any may include, store, communicating, propagating
Or transfer program uses for instruction execution system, device or equipment or in conjunction with these instruction execution systems, device or equipment
Device.The more specific example (non-exhaustive list) of computer-readable medium include the following: there are one or more wirings
Electrical connection section (electronic device), portable computer diskette box (magnetic device), random access memory (RAM), read-only memory
(ROM), erasable edit read-only storage (EPROM or flash memory), fiber device and portable optic disk are read-only
Memory (CDROM).In addition, computer-readable medium can even is that the paper that can print described program on it or other conjunctions
Suitable medium, because can then be edited for example by carrying out optical scanner to paper or other media, be interpreted or necessary
When handled with other suitable methods electronically to obtain described program, be then stored in computer storage
In.
It should be appreciated that each section of the invention can be realized with hardware, software, firmware or their combination.Above-mentioned
In embodiment, multiple steps or method can be executed soft in memory and by suitable instruction execution system with storage
Part or firmware are realized.Such as, if realized with hardware in another embodiment, can be under well known in the art
Any one of column technology or their combination are realized: having a logic gate electricity for realizing logic function to data-signal
The discrete logic on road, the specific integrated circuit with suitable combinational logic gate circuit, programmable gate array (PGA) are existing
Field programmable gate array (FPGA) etc..
Those skilled in the art are understood that realize all or part of step that above-described embodiment method carries
It suddenly is that relevant hardware can be instructed to complete by program, the program can store in a kind of computer-readable storage
In medium, which when being executed, includes the steps that one or a combination set of embodiment of the method.
It, can also be in addition, each functional unit in each embodiment of the present invention can integrate in a processing module
It is that each unit physically exists alone, can also be integrated in two or more units in a module.Above-mentioned integrated mould
Block both can take the form of hardware realization, can also be realized in the form of software function module.The integrated module is such as
Fruit is realized and when sold or used as an independent product in the form of software function module, also can store and calculates at one
In machine read/write memory medium.
Storage medium mentioned above can be read-only memory, disk or CD etc..Although having been shown and retouching above
The embodiment of the present invention is stated, it is to be understood that above-described embodiment is exemplary, and should not be understood as to limit of the invention
System, those skilled in the art above-described embodiment can be changed, be modified within the scope of the invention, replaced and
Modification.
Claims (14)
1. a kind of parameters weighting based on path planning determines method, which is characterized in that the described method comprises the following steps:
According to a variety of candidate weight combinations of vehicle driving parameter, multiple input information are generated；
The multiple input information is inputted into path planning model respectively, obtains corresponding a plurality of path candidate；
Determine the similarity between a plurality of path candidate and practical driving path；
Determine that target weight combines from a variety of candidate weight combinations according to the similarity.
2. parameters weighting according to claim 1 determines method, which is characterized in that it is described according to the similarity, from institute
It states in a variety of candidate weight combinations, after determining that target weight combines, further includes:
It is combined according to the target weight, adjusts the weight of the vehicle driving parameter；
Path planning is carried out according to the vehicle driving parameter of adjustment weight.
3. parameters weighting according to claim 2 determines method, which is characterized in that each Driving Scene has corresponding target
Weight combination；
It is described to be combined according to the target weight, adjust the weight of the vehicle driving parameter, comprising:
According to the corresponding target weight combination of each Driving Scene, the power of the vehicle driving parameter under corresponding Driving Scene is adjusted
Weight.
4. parameters weighting according to claim 3 determines method, which is characterized in that the vehicle driving parameter, which has, to be corresponded to
The multiple groups detected value of similar Driving Scene；It is described that target is determined from a variety of candidate weight combinations according to the similarity
Weight combination, comprising:
Determine the vehicle driving parameter take each group described in detected value when, the similarity meets the candidate weight of setting condition
Combination；
Meet the similarity the candidate weight combination statistics frequency of occurrences of setting condition；
According to the frequency of occurrences, from the candidate weight combination that the similarity meets setting condition, the target power is determined
Recombination.
5. parameters weighting according to claim 1 determines method, which is characterized in that the determination a plurality of path candidate
With the similarity between practical driving path, comprising:
Using dynamic time warping algorithm, the similarity between a plurality of path candidate and practical driving path is determined.
6. parameters weighting according to claim 1-5 determines method, which is characterized in that the vehicle driving parameter
It include: one or more combinations in traveling-position, car speed, vehicle acceleration and vehicle yaw angle.
7. a kind of parameters weighting determining device based on path planning, which is characterized in that described device includes:
Generation module generates multiple input information for a variety of candidate weight combinations according to vehicle driving parameter；
Input module obtains corresponding a plurality of candidate road for the multiple input information to be inputted path planning model respectively
Diameter；
First determining module, for determining the similarity between a plurality of path candidate and practical driving path；
Second determining module, for determining target weight group from a variety of candidate weight combinations according to the similarity
It closes.
8. parameters weighting determining device according to claim 7, which is characterized in that described device, further includes:
Module is adjusted, for combining according to the target weight, adjusts the weight of the vehicle driving parameter；
Planning module, for carrying out path planning according to the vehicle driving parameter of adjustment weight.
9. parameters weighting determining device according to claim 7, which is characterized in that each Driving Scene has corresponding target
Weight combination；The adjustment module adjusts under corresponding Driving Scene for being combined according to the corresponding target weight of each Driving Scene
The weight of the vehicle driving parameter.
10. parameters weighting determining device according to claim 9, which is characterized in that the vehicle driving parameter have pair
Answer the multiple groups detected value of similar Driving Scene；Second determining module, comprising:
First determination unit, for determine the vehicle driving parameter take each group described in detected value when, the similarity meets
The candidate weight of setting condition combines；
Statistic unit, the candidate weight for meeting the similarity setting condition combine the statistics frequency of occurrences；
Second determination unit, for meeting the candidate weight combination of setting condition from the similarity according to the frequency of occurrences
In, determine the target weight combination.
11. parameters weighting according to claim 7 determines method, which is characterized in that first determining module, for adopting
With dynamic time warping algorithm, the similarity between a plurality of path candidate and practical driving path is determined.
12. parameters weighting according to claim 1-5 determines method, which is characterized in that the vehicle driving ginseng
Number includes: one or more combinations in traveling-position, car speed, vehicle acceleration and vehicle yaw angle.
13. a kind of computer equipment, which is characterized in that including memory, processor and store on a memory and can handle
The computer program run on device when the processor executes described program, realizes such as base as claimed in any one of claims 1 to 6
Method is determined in the parameters weighting of path planning.
14. a kind of non-transitorycomputer readable storage medium, is stored thereon with computer program, which is characterized in that the program
It is realized when being executed by processor as the parameters weighting as claimed in any one of claims 1 to 6 based on path planning determines method.
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Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112686444A (en) * | 2020-12-29 | 2021-04-20 | 北京中交兴路信息科技有限公司 | Truck route planning method and device based on similar vehicle driving characteristic data |
| CN113085895A (en) * | 2021-04-19 | 2021-07-09 | 陕西理工大学 | Vehicle lane change track planning method, device, equipment, storage medium and vehicle |
| CN113741180A (en) * | 2020-11-30 | 2021-12-03 | 北京京东乾石科技有限公司 | Online updating method and device for automatic driving vehicle model |
| CN114291111A (en) * | 2021-12-30 | 2022-04-08 | 广州小鹏自动驾驶科技有限公司 | Target path determination method, target path determination device, vehicle and storage medium |
| CN117496745A (en) * | 2023-10-09 | 2024-02-02 | 上海软杰智能设备有限公司 | Parking space guidance data analysis system and method based on artificial intelligence |
Citations (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2006234429A (en) * | 2005-02-22 | 2006-09-07 | Navitime Japan Co Ltd | Navigation system, route search server, and route search method |
| CN104165625A (en) * | 2013-05-17 | 2014-11-26 | 北京百度网讯科技有限公司 | Path planning method and equipment |
| CN104422459A (en) * | 2013-09-06 | 2015-03-18 | 上海博泰悦臻电子设备制造有限公司 | Method and device for determining location of vehicle as well as navigation method and navigation system |
| CN104819728A (en) * | 2015-04-23 | 2015-08-05 | 太仓红码软件技术有限公司 | Path navigation time calculating method based on gradient and slope |
| CN105599764A (en) * | 2014-11-12 | 2016-05-25 | 现代自动车株式会社 | Driving path planning apparatus and method for autonomous vehicle |
| CN105679037A (en) * | 2016-03-29 | 2016-06-15 | 重庆云途交通科技有限公司 | Dynamic path planning method based on user travel habit |
| CN106846901A (en) * | 2015-12-03 | 2017-06-13 | 财团法人资讯工业策进会 | Avoid the System and method for of abnormal vehicle |
| CN107491079A (en) * | 2017-10-12 | 2017-12-19 | 广东嘉腾机器人自动化有限公司 | Automatical pilot transportation vehicle control method, device and computer-readable recording medium |
| CN108805314A (en) * | 2017-04-27 | 2018-11-13 | 纵目科技（上海）股份有限公司 | One kind is parked optimal route selection method and system, car-mounted terminal |
| CN109059944A (en) * | 2018-06-06 | 2018-12-21 | 上海国际汽车城（集团）有限公司 | Motion planning method based on driving habit study |
| CN109436174A (en) * | 2018-09-28 | 2019-03-08 | 北京摩拜科技有限公司 | Control method for vehicle, server and Vehicular system |
-
2019
- 2019-04-04 CN CN201910270512.2A patent/CN110045734A/en active Pending
Patent Citations (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2006234429A (en) * | 2005-02-22 | 2006-09-07 | Navitime Japan Co Ltd | Navigation system, route search server, and route search method |
| CN104165625A (en) * | 2013-05-17 | 2014-11-26 | 北京百度网讯科技有限公司 | Path planning method and equipment |
| CN104422459A (en) * | 2013-09-06 | 2015-03-18 | 上海博泰悦臻电子设备制造有限公司 | Method and device for determining location of vehicle as well as navigation method and navigation system |
| CN105599764A (en) * | 2014-11-12 | 2016-05-25 | 现代自动车株式会社 | Driving path planning apparatus and method for autonomous vehicle |
| CN104819728A (en) * | 2015-04-23 | 2015-08-05 | 太仓红码软件技术有限公司 | Path navigation time calculating method based on gradient and slope |
| CN106846901A (en) * | 2015-12-03 | 2017-06-13 | 财团法人资讯工业策进会 | Avoid the System and method for of abnormal vehicle |
| CN105679037A (en) * | 2016-03-29 | 2016-06-15 | 重庆云途交通科技有限公司 | Dynamic path planning method based on user travel habit |
| CN108805314A (en) * | 2017-04-27 | 2018-11-13 | 纵目科技（上海）股份有限公司 | One kind is parked optimal route selection method and system, car-mounted terminal |
| CN107491079A (en) * | 2017-10-12 | 2017-12-19 | 广东嘉腾机器人自动化有限公司 | Automatical pilot transportation vehicle control method, device and computer-readable recording medium |
| CN109059944A (en) * | 2018-06-06 | 2018-12-21 | 上海国际汽车城（集团）有限公司 | Motion planning method based on driving habit study |
| CN109436174A (en) * | 2018-09-28 | 2019-03-08 | 北京摩拜科技有限公司 | Control method for vehicle, server and Vehicular system |
Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113741180A (en) * | 2020-11-30 | 2021-12-03 | 北京京东乾石科技有限公司 | Online updating method and device for automatic driving vehicle model |
| CN112686444A (en) * | 2020-12-29 | 2021-04-20 | 北京中交兴路信息科技有限公司 | Truck route planning method and device based on similar vehicle driving characteristic data |
| CN112686444B (en) * | 2020-12-29 | 2024-02-09 | 北京中交兴路信息科技有限公司 | Truck route planning method and device based on similar vehicle driving characteristic data |
| CN113085895A (en) * | 2021-04-19 | 2021-07-09 | 陕西理工大学 | Vehicle lane change track planning method, device, equipment, storage medium and vehicle |
| CN114291111A (en) * | 2021-12-30 | 2022-04-08 | 广州小鹏自动驾驶科技有限公司 | Target path determination method, target path determination device, vehicle and storage medium |
| CN114291111B (en) * | 2021-12-30 | 2024-03-08 | 广州小鹏自动驾驶科技有限公司 | Target path determination method, device, vehicle and storage medium |
| CN117496745A (en) * | 2023-10-09 | 2024-02-02 | 上海软杰智能设备有限公司 | Parking space guidance data analysis system and method based on artificial intelligence |
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